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How Geometry Helps in Data Analysis and ML Problems
October 20, 2022 @ 7:00 pm - 9:00 pm +04
About the Event:
We are excited to announce that PyData Yerevan 4th Monthly Meetup is on its way!
Our speaker Denis Fedoseev, who obtained Ph.D. in Mathematics, will give a talk on “How Geometry Helps in Data Analysis and ML Problems?”
Many ML models work with geometrical representations of data: from simple linear regression to convoluted NN algorithms sending images to vectors in unspeakably high-dimensional feature space. That makes geometry and, in particular, differential geometry a powerful tool to tackle ML problems.
In this talk, Denis Fedoseev will give a brief outline of the geometrical concepts and methods which can be used in this context, including:
-the general notion of manifold (which will help to understand the Manifold hypothesis in data science)
-tangent space
-Riemannian metric
-Minkowski dimension
Save the date to join us on October 20, at 19:00, in the 314W PAB room at the American University of Armenia to explore more with the PyData Yerevan community.
About the Speaker:
Denis Fedoseev has obtained Ph.D. in Mathematics specializing in geometry and topology from Moscow State University in 2015 and since then worked in the field of knot theory and related areas of low-dimensional topology.
For the last two years participated in a joint project of MSU and Huawei corporation on the application of geometric methods in computer vision problems. Since then, got fascinated with the world of machine learning, neural networks, and AI capabilities.
Language: English
